Shagan Sah, Jan A. N. van Aardt, Donald M. McKeown, and David W. Messinger

“Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes – forest, urban, water and vegetation – using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.”

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The first session, LiDAR 101, has been archived and is available for purchase. Please visit www.urisa.org/urisaconnect to learn more about viewing archived URISA Connect webinars.

The purpose of this webinar series is to review concepts involving light detection and ranging (LiDAR), its real-world applications, current visualization, data processing and quality control approaches. The target audience of this webinar is at the beginner to intermediate level, including GIS managers, geospatial analysts, survey professionals, scientists and students.

Series presenter, Mark Stucky, currently serves as the MARS® Technical Support Specialist for the GeoSpatial Solutions division of Merrick & Company, a professional engineering services firm based in Aurora, Colorado (USA). In this position, Mark is responsible for technical support and software maintenance of MARS®, Merrick’s LiDAR visualization and data processing software solution. Mark is extensively involved in MARS® design, testing, and documentation.

URISA Connect is an effective way to develop your professional skills and even work towards GISCI Certification Education Hours. URISA Webinars count towards the EDU-2 section of the application.

For more information about this series, or to register, please visit www.urisa.org/uclidar.

Other upcoming URISA Connect events include the return of the popular Asset Management series. For more information, please visit www.urisa.org/urisaconnect.

[Source: URISA press release]

Mauro CAPRIOLI and Alfredo SCOGNAMIGLIO,

“In the last years the topic of Environmental monitoring has raised a particular importance, also according to minor short-term stability and predictability of climatic events. Facing this situation, often in terms of emergency, involves high and unpredictable costs for public Agencies.

Change detection before and after filter application

“Prevention of damages caused by natural disasters does not regards only weather forecasts, but implies the constant attention and practice of monitoring and control of human activity on territory. Practically, the problem is not knowing if and when an event will affect a determined area, but recognizing the possible damages if this event happened, by adopting the adequate measures to reduce them to a minimum, and requiring the necessary tools for a timely intervention. On the other hand, the surveying technologies should be the most possible accurate and updatable in order to guarantee high standards, involving the analysis of a great amount of data. The management of such data requires the integration and calculation systems with specialized software and fast and reliable connection and communication networks.

“Change detection analysis serve to facilitate individuation of environmental temporal variations, contributing to reduce the users intervention by means of the processes automation and improving in a progressive way the qualitative and quantitative accuracy of results. The research investigate automatic methods on land cover transformations by means of “Change detection” techniques executable on satellite data that are heterogeneous for spatial and spectral resolution with homogenization and registration in an unique digital information environment.

“In the present work we tested some areas of study particularly interesting for the knowledge of the morphology changes of land cover, in particular the area of the coast of Manfredonia characterized by the presence of important industrial sites and protected area of the Park of Alta Murgia with frequent episodes of land transformation.”

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A researcher is shown climbing into the canopy of a primary forest in Southern Cameroon to measure tree height and crown dimensions. Photo: Courtesy of authors.

Earthzine, posted on April 24th, 2012

Pierre Couteron, Nicolas Barbier, Christophe Proisy, Raphaël Pélissier, and Grégoire Vincent

“Using remote sensing to provide reliable information over extensive areas of dense and heterogeneous tropical forests is a challenging task. Not only is the task challenging, but it also has become closely related to global concerns about reducing greenhouse gas emissions from deforestation and forest degradation, also known as the REDD process. The AMAP laboratory in Montpellier, France, is contributing to this challenge at the interface between signal processing and plant and vegetation modelling which is its central domain of expertise. Models of forest structure are an important tool to fill the scale gap between field observations and remotely sensed information. They help also to understand the complex interactions between signal and forest vegetation. As remotely-sensed data are diversifying, coupling forest structure and radiative transfer models helps to translate signal information into biophysical parameters. Refining such an approach is needed to design replicable methods that address the most challenging aspect of monitoring spatiotemporal variations of stand structure in forest types retaining high aboveground biomass.”

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Tomaž Podobnikar

“The detection of peaks (summits) as the upper parts of mountains and the delineation of their shape is commonly confirmed by inspections carried out by mountaineers. In this study the complex task of peak detection and shape delineation is solved by autometric methodological procedures, more precisely, by developing relatively simple but innovative image-processing and spatial-analysis techniques (e.g., developing inventive variables using an annular moving window) in remote sensing and GIS domains. The techniques have been integrated into automated morphometric methodological procedures. The concepts of peaks and their shapes (sharp, blunt, oblong, circular and conical) were parameterized based on topographic and morphologic criteria.

“A geomorphologically high quality DEM was used as a fundamental dataset. The results, detected peaks with delineated shapes, have been integratively enriched with numerous independent datasets (e.g., with triangulated spot heights) and information (e.g., etymological information), and mountaineering criteria have been implemented to improve the judgments. This holistic approach has proved the applicability of both highly standardized and universal parameters for the geomorphologically diverse Kamnik Alps case study area. Possible applications of this research are numerous, e.g., a comprehensive quality control of DEM or significantly improved models for the spatial planning proposes.”

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International Journal of Geographical Information Science

Shiuan Wan, Tsu-Chiang Lei & Tein-Yin Chou

“Remote Sensing (RS) data can assist in the classification of landscapes to identify landslides. Recognizing the relationship between landform/landscape and landslide areas is, however, complex. Soil properties, geomorphological, and groundwater conditions govern the instability of slopes. Previous study of Wan (2009; A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108, 237–251) used the maximum-likelihood classifier to classify the multi-category landslide image data. Unfortunately, the classification does not consider the geomorphologic condition. Accordingly, a Landslide Expert System was developed to modify these problems. The system uses multi-date SPOT image data to develop the landslide database. The threshold slope which becomes vulnerable to landslides is obtained by the K-means method. Then, an innovative Data Mining technique – Discrete Rough Sets (DRS) – is applied to obtain the core variables and their relevant thresholds. Finally, the Expert Knowledge Translation Platform (EKTP) is used to create the rules for classification. This study used a new approach called ‘Rough Set Tree’ to demonstrate the performance of the approach. The classification of landslide vulnerable areas, bare land, rock, streams, and water-body is greatly improved.”

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Thomas Adams, Peter Beets and Christopher Parrish

“Light Detection And Ranging (LiDAR) in forested areas is used for constructing Digital Terrain Models (DTMs), estimating biomass carbon and timber volume and estimating foliage distribution as an indicator of tree growth and health. All of these purposes are hindered by the inability to distinguish the source of returns as foliage, stems, understorey and the ground except by their relative positions. The ability to separate these returns would improve all analyses significantly. Furthermore, waveform metrics providing information on foliage density could improve forest health and growth estimates.

Bivariate frequency distribution of peak height of Gaussian curves fitted to
deconvolved waveform LiDAR vs. height above ground.

“In this study, the potential to use waveform LiDAR was investigated. Aerial waveform LiDAR data were acquired for a New Zealand radiata pine plantation forest, and Leaf Area Density (LAD) was measured in the field. Waveform peaks with a good signal-to-noise ratio were analyzed and each described with a Gaussian peak height, half-height width, and an exponential decay constant. All parameters varied substantially across all surface types, ruling out the potential to determine source characteristics for individual returns, particularly those with a lower signal-to-noise ratio. However, pulses on the ground on average had a greater intensity, decay constant and a narrower peak than returns from coniferous foliage. When spatially averaged, canopy foliage density (measured as LAD) varied significantly, and was found to be most highly correlated with the volume-average exponential decay rate. A simple model based on the Beer-Lambert law is proposed to explain this relationship, and proposes waveform decay rates as a new metric that is less affected by shadowing than intensity-based metrics. This correlation began to fail when peaks with poorer curve fits were included.”

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Fernando Sedano, Pieter Kempeneers, Peter Strobl, Daniel McInerney and Jesús San Miguel

“A two stage burned scar detection approach is applied to produce a burned scar map for Mediterranean Europe using IRS-AWiFS imagery acquired at the end of the 2009 fire season. The first stage identified burned scar seeds based on a learning algorithm (Artificial Neural Network) coupled with a bootstrap aggregation process. The second stage implemented a region growing process to extend the area of the burned scars. Several ancillary datasets were used for the accuracy assessment and a final visual check was performed to refine the burned scar product. Training data for the learning algorithm were obtained from MODIS-based polygons, which were generated by the Rapid Damage Assessment module of the European Forest Fire Information System. The map produced from this research is the first attempt to increase the spatial detail of current burned scar maps for the Mediterranean region. The map has been analyzed and compared to existing burned area polygons from the European Forest Fire Information System. The comparison showed that the IRS-AWiFS-based burned scar map improved the delineation of burn scars; in addition the process identified a number of small burned scars that were not detected on lower resolution sensor data. Nonetheless, the results do not clearly support the improved capability for the detection of smaller burned scars.

Snapshots in North-west Spain (ETRS89/LAEA projection) corresponding to:
(A) Near infrared IRS-AWiFS; (B) EFFIS-RDA burned scar; (C) IRS-AWiFS-based
burned scars.

“A number of reasons can be provided for the under-detection of burned scars, these include: the lack of a full coverage and cloud free imagery, the time lag between forest fires and image acquisition date and the occurrence of fires after the image acquisition dates. On the other hand, the limited spectral information combined with the presence of undetected cloud shadows and shaded slopes are reasons for the over-estimation of small burned scars.”

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GeoInformatica

Lei Wang, Andrew G. Birt, Charles W. Lafon, David M. Cairns, Robert N. Coulson, Maria D. Tchakerian, Weimin Xi, Sorin C. Popescu and James M. Guldin

“Small Footprint LiDAR (Light Detection And Ranging) has been proposed as an effective tool for measuring detailed biophysical characteristics of forests over broad spatial scales. However, by itself LiDAR yields only a sample of the true 3D structure of a forest. In order to extract useful forestry relevant information, this data must be interpreted using mathematical models and computer algorithms that infer or estimate specific forest metrics. For these outputs to be useful, algorithms must be validated and/or calibrated using a sub-sample of ‘known’ metrics measured using more detailed, reliable methods such as field sampling. In this paper we describe a novel method for delineating and deriving metrics of individual trees from LiDAR data based on watershed segmentation. Because of the costs involved with collecting both LiDAR data and field samples for validation, we use synthetic LiDAR data to validate and assess the accuracy of our algorithm. This synthetic LiDAR data is generated using a simple geometric model of Loblolly pine (Pinus taeda) trees and a simulation of LiDAR sampling. Our results suggest that point densities greater than 2 and preferably greater than 4 points per m2 are necessary to obtain accurate forest inventory data from Loblolly pine stands. However the results also demonstrate that the detection errors (i.e. the accuracy and biases of the algorithm) are intrinsically related to the structural characteristics of the forest being measured. We argue that experiments with synthetic data are directly useful to forest managers to guide the design of operational forest inventory studies. In addition, we argue that the development of LiDAR simulation models and experiments with the data they generate represents a fundamental and useful approach to designing, improving and exploring the accuracy and efficiency of LiDAR algorithms.”

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Oumar Allafouza Loni, M. Tahir Hussein and Ayman M. Alrehaili

“Landfilled wastes manifest slow decomposition, producing emanation of gases, and outflow of leachate. Waste mass shows various chemical reactions and complex evolutions that occur under the influence of natural agents, as rain and microorganisms. These reactions lead to biological, physical, and chemical transformations of wastes. The intensity of the phenomenon is related to the air and the humidity. These factors originate from the initial composition of the solid waste, the operating mode of the landfill, and the geological and hydrogeological conditions. Leachate is considered a major source of groundwater pollution. It has a complex nature; it typically contains high concentrations of chemical hazardous including heavy metals, chemical compounds that may severely pollute the environment. These challenges are faced all over the world by environment protection agencies and waste management bodies. The challenge differs according to the specific situation of the site, the climatic, environmental, and geological factors. The international literature is rich with studies in this concern. Each country or region of the world has its own legislation and laws governing waste management, e.g., the European Commission Legislation, the US Environmental Agency, and so forth. The main objective of this study is to shed light on the environmental consequences of a landfill site located in the southeast of Riyadh City, Saudi Arabia. It constitutes a peculiar case because of its situation, its exploitation mode, and nature of buried wastes. The study made use of satellite MSS, TM, ETM and SPOT image 2007, and Digital Elevation Model (DEM), respectively. Geological, morphological, hydrological, hydrochemical, and detailed drainage analyses were performed. Records of meteorological stations were also used in this study. The satellite images illustrate the evolution of the site through time since its start in the 1990s of the twentieth century. The main geological units outcropping in the area are the Sulaiy Formation, the Yamama Formation, Khabra deposits, floodplain deposits, alluvium, and sheet gravel. Drainage analyses shows a dendritic nature for the network, a total area of 2,113 km2, basin slope of 0.016, perimeter of 430 × 103, and a mean elevation of 635 m. Annual rainfall is around 100 mm, evapotranspiration is about 2,900 mm, wind speed averages at 5.1 km/h, and runoff peak is within 2.7–4.7 m3/s. A plume of total dissolved solids and nitrates was observed to initiate from the landfill site. Heavy metal concentration confirms the same result. Planners, environmentalists, decision makers, and other interest groups can use the findings of this study for environmental management of the landfill and protection of the downstream part of the Sulaiy tributary from leachate contamination. The results indicate the importance of monitoring landfills through the combined use of ground and satellite monitoring.”

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